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Posts from the ‘Tech Careers & Workplace’ Category

Jul 10

What’s Next For You? How AI Is Transforming Talent Management

Bottom Line: Taking on the talent crisis with greater intelligence and insight, delivering a consistently excellent candidate experience, and making diversity and inclusion a part of their DNA differentiates growing businesses who are attracting and retaining employees. The book What’s Next For You? by Ashutosh Garg, CEO and Co-Founder and Kamal Ahluwalia, President of eightfold.ai provide valuable insights and a data-driven roadmap of how AI is helping to solve the talent crisis for any business.

The Talent Crisis Is Real

  • 78% of CEOs and Chief Human Resource Officers (CHROs) say talent programs are important, with 56% say their current programs are ineffective.
  • 83% of employees want a new job yet only 53% want to leave for a new company.
  • 57% of employees say diversity and inclusion initiatives aren’t working, and 40% say their companies lack qualified diverse talent.
  • Nearly 50% of an organizations’ top talent will leave their jobs in the first two years of being hired.
  • 28% of open positions today won’t be filled in the next 12 months.

The above findings are just a sample of the depth of data-driven content and roadmap the book What’s Next For You? delivers. Co-authors Ashutosh Garg’s and Kamal Ahluwalia’s expertise in applying AI and machine learning to talent management problems with a strong data-first mindset is evident throughout the book. What makes the book noteworthy is how the authors write from the heart first with empathy for applicants and hiring managers, supporting key points with data. The empathetic, data-driven tone of the book makes the talent crisis relatable while also illustrating how AI can help any business make better talent management decisions.

“Businesses are having to adapt to technology changes and changes in customer expectations roughly every 10 years – a timeframe that is continuing to shrink. As a result, business leaders need to really focus on rethinking their business strategy and the associated talent strategy, so they have the organizational capability to transform and capitalize on the inevitable technology shifts,” writes John Thompson, Venture Partner, Lightspeed Venture Partners and Chairman of the Board at Microsoft in the forward.

The book cites talent management researchers and experts who say “our current knowledge base has a half-life of about two years, and the speed of technology is outperforming us as humans because of what it can do quickly and effectively“ (p.64). John Thompson’s observations in the forward that the time available for adapting to change is shrinking is a unifying thread that ties this book together. One of the most convincing is the fact that using today’s Applicant Tracking Systems (ATS) and hiring processes prone to biases, there’s a 30% chance a new hire will not make it through their first year. If the new hire is a cloud computing professional, this equates to a median salary of $146,350 and taking best-case 46 days to find their replacement. The cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. Any manager who has lost a new hire within a year can relate to how real the talent crisis is and how urgent it is to solve it.

The Half-Life Of Skills Is Shrinking Fast

The most compelling chapter of the book illustrates how today’s talent crisis can be solved by taking an AI-enabled approach to every aspect of talent management. Chapter 4, The Half-Life Of Skills Is Shrinking Fast, delves into how AI can find candidates who can unlearn old concepts, and quickly master new ones. The book calls out this attribute of any potential new hire as being essential for them to adapt.  Using higher quality data than is available in traditional ATS systems, the authors illustrate how AI-based systems can be used for evaluating both the potential and experiences of applicants to match them with positions they will excel in. The authors make a convincing argument that AI can increase the probability of new candidate success. They cite a well-known Leadership IQ statistic of 46% of all new employee hires failing to adapt within 18 months, and the Harvard Business Review study finding between 40% to 60% of new upper management hires fail within 18 months. The authors contend that even Leonardo Da Vinci, one of the primary architects of the Renaissance, would have trouble finding work using a traditional resume entered into an ATS system today because his exceptional capabilities and potential would have never been discovered. When our existing process of recruiting is based on practices over 500 years old, as this copy of Leonardo Da Vinci’s resume illustrates, it’s time to put AI to work matching peoples’ potential with unique position requirements.

When Employees Achieve Their Potential, Companies Do Too   

Attracting the highest potential employees possible and retaining them is the cornerstone of any digital business’ growth strategy today and in the future. The book addresses the roadblocks companies face in attaining that goal, with bias being one of the strongest. “For example, McKinsey & Co., a top consulting agency, studied over 1,000 companies across 12 countries and found that firms in the top quartile of gender diversity were a fifth more likely to have above-average profits than those in the bottom quartile,” (p. 105). Further, “diverse executive boards generate better financial returns, and gender-diverse teams are more creative, more productive and more confident.” (p. 105).

In conclusion, consider this book a roadmap of how hiring and talent management can change for the better based on AI. The authors successfully illustrate how combining talent, personalization at scale, and machine learning can help employees achieve their potential, enabling companies to achieve theirs in the process.

Jun 13

How To Get Your Data Scientist Career Started

The most common request from this blogs’ readers is how to further their careers in analytics, cloud computing, data science, and machine learning. I’ve invited Alyssa Columbus, a Data Scientist at Pacific Life, to share her insights and lessons learned on breaking into the field of data science and launching a career there. The following guest post is authored by her.

Earning a job in data science, especially your first job in data science, isn’t easy, especially given the surplus of analytics job-seekers to analytics jobs.

Many people are looking to break into data science, from undergraduates to career changers, have asked me how I’ve attained my current data science position at Pacific Life. I’ve referred them to many different resources, including discussions I’ve had on the Dataquest.io blog and the Scatter Podcast. In the interest of providing job seekers with a comprehensive view of what I’ve learned that works, I’ve put together the five most valuable lessons learned. I’ve written this article to make your data science job hunt easier and as efficient as possible.

  • Continuously build your statistical literacy and programming skills. Currently, there are 24,697 open Data Scientist positions on LinkedIn in the United States alone. Using data mining techniques to analyze all open positions in the U.S., the following list of the top 10 data science skills was created today. As of April 14, the top 3 most common skills requested in LinkedIn data scientist job postings are Python, R, and SQL, closely followed by Jupyter Notebooks, Unix Shell/Awk, AWS, and Tensorflow. The following graphic provides a prioritized list of the most in-demand data science skills mentioned in LinkedIn job postings today. Please click on the graphic to expand for easier viewing.

Hands-on training is the best way to develop and continually improve statistical and programming skills, especially with the languages and technologies LinkedIn’s job postings prioritize.  Getting your hands dirty with a dataset is often much better than reading through abstract concepts and not applying what you’ve learned to real problems. Your applied experience is just as important as your academic experience, and taking statistics, and computer science classes help to translate theoretical concepts into practical results. The toughest thing to learn (and also to teach) about statistical analysis is the intuition for what the big questions to ask of your dataset are. Statistical literacy, or “how” to find the answers to your questions, come with education and practice. Strengthening your intellectual curiosity or insight into asking the right questions comes through experience.

  • Continually be creating your own, unique portfolio of analytics and machine learning projects. Having a good portfolio is essential to be hired as a data scientist, especially if you don’t come from a quantitative background or have experience in data science before. Think of your portfolio as proof to potential employers that you are capable of excelling in the role of a data scientist with both the passion and skills to do the job. When building your data science portfolio, select and complete projects that qualify you for the data science jobs, you’re the most interested in. Use your portfolio to promote your strengths and innate abilities by sharing projects you’ve completed on your own. Some skills I’d recommend you highlight in your portfolio include:
    • Your programming language of choice (e.g., Python, R, Julia, etc.).
    • The ability to interact with databases (e.g., your ability to use SQL).
    • Visualization of data (static or interactive).
    • Storytelling with data. This is a critical skill. In essence, can someone with no background in whatever area your project is in look at your project and gain some new understandings from it?
    • Deployment of an application or API. This can be done with small sample projects (e.g., a REST API for an ML model you trained or a nice Tableau or R Shiny dashboard).

Julia Silge and Amber Thomas both have excellent examples of portfolios that you can be inspired by. Julia’s portfolio is shown below.

  • Get (or git!) yourself a website. If you want to stand out, along with a portfolio, create and continually build a strong online presence in the form of a website.  Be sure to create and continually add to your GitHub and Kaggle profiles to showcase your passion and proficiency in data science. Making your website with GitHub Pages creates a profile for you at the same time, and best of all it’s free to do. A strong online presence will not only help you in applying for jobs, but organizations may also reach out to you with freelance projects, interviews, and other opportunities.
  • Be confident in your skills and apply for any job you’re interested in, starting with opportunities available in your network.  If you don’t meet all of a job’s requirements, apply anyway. You don’t have to know every skill (e.g., programming languages) on a job description, especially if there are more than ten listed. If you’re a great fit for the main requirements of the job’s description, you need to apply. A good general rule is that if you have at least half of the skills requested on a job posting, go for it. When you’re hunting for jobs, it may be tempting to look for work on company websites or tech-specific job boards. I’ve found, as have many others, that these are among the least helpful ways to find work. Instead, contact recruiters specializing in data science and build up your network to break into the field. I recommend looking for a data science job via the following sources, with the most time devoted to recruiters and your network:
    • Recruiters
    • Friends, family, and colleagues
    • Career fairs and recruiting events
    • General job boards
    • Company websites
    • Tech job boards.

Alyssa Columbus is a Data Scientist at Pacific Life and member of the Spring 2018 class of NASA Datanauts. Previously, she was a computational statistics and machine learning researcher at the UC Irvine Department of Epidemiology and has built robust predictive models and applications for a diverse set of industries spanning retail to biologics. Alyssa holds a degree in Applied and Computational Mathematics from the University of California, Irvine and is a member of Phi Beta Kappa. She is a strong proponent of reproducible methods, open source technologies, and diversity in analytics and is the founder of R-Ladies Irvine. You can reach her at her website: alyssacolumbus.com.

Jun 13

The State Of 3D Printing, 2019

  • Proof of concept and prototyping dominate 3D printing applications in 2019.
  • 80% of enterprises say 3D printing is enabling them to innovate faster.
  • 51% of enterprises are actively using 3D printing in production.

These and many other fascinating insights are from Sculpteo’s 5th edition of their popular study, The State of 3D Printing (29 pp., PDF, opt-in). The study’s methodology is based on interviews with 1,300 respondents coming from Europe (64%), United States (16.6 %) and Asia (20.2%), which is the fastest growing region internationally today as measured by this survey over five years. Eight industries are included in the research design including Industrial Goods (13.6%), High Tech (10.6%), Services (9.9%), Consumer Goods (8.6%), Health & Medical (6.2%), Automotive (5.7%), Aerospace & Defense (5.5%), and Education (4.9%). For additional details on the methodology, please see pages 6 and 7 of the study. Key takeaways from the survey include the following:

  • Proof of concepts and prototyping dominate 3D printing applications in 2019. Manufacturers are increasing their reliance on 3D printing as part of their broader manufacturing strategies, with production use up to 51% of all respondents from 38.7% in 2018. The following compares 2019’s purpose of 3D prints versus the last five years of survey data. Please click on the graphic to expand for easier reading.

  • Accelerating product development continues to be enterprises’ top focus guiding their 3D printing strategies in 2019. Mass customization and support for configure-to-order and engineer-to-order product strategies also continue to be a high priority this year, continued the trend since 2015. Increasing production flexibility is the third area of focus that guides additive manufacturing strategies today. Please click on the graphic to expand for easier reading.

  • Nearly 50% of enterprises say that quality control is their top challenge of using their 3D printers. As enterprises increase their adoption of 3D printing to accelerate their additive manufacturing strategies, quality is becoming increasingly more important. Manufacturers most define their success by the perceived level of quality products they deliver to their customers, which is increasing quality control as a needed benefit of 3D printing. Please click on the graphic to expand for easier reading.

  • Adopting a design-to-manufacturing strategy accelerates new product development and innovation, which is why CAD design leads all other activities today. When responses were asked which areas related to 3D printing and additive manufacturing, they spend the majority of their time, nearly 50% said CAD design. Building prototypes, research, and testing prototypes are also areas those enterprises adopting additive manufacturing are investing in today. Please click on the graphic to expand for easier reading.

  • Additive manufacturing adoption is growing across shop floors globally, evidenced by more than 70% of enterprises finding new applications for 3D printing in 2019 and 60% using CAD, simulation, and reverse engineering internally. The leading indicators of additive manufacturing becoming more pervasively adopted across global shop floors are shown in the following graphic. New uses for 3D printing, experimenting with new materials, extensive CAD Design integration combined with simulation and reverse engineering provide further evidence of how engrained additive manufacturing is becoming in production processes daily. 3D printing is now most commonly used alongside CNC machining, another strong indicator of how essential additive manufacturing is becoming to the production process. Please click on the graphic to expand for easier reading.

  • 3D printings’ innate strengths at producing items with complex geometries at a quick pace or iteration are the leading two benefits of 3D printing in 2019. More than 40% of enterprises say that rapid iterations of prototypes and lead time reductions are the leading benefits followed by mass customization (support for configure-to-order & engineer-to-order product strategies) and cost savings. Please click on the graphic to expand for easier reading.

  • 80% of high tech manufacturing respondents are relying on 3D printing for prototyping, leading all industries in this category. 47% use 3D printing to accelerate product development. High tech manufacturers are above average in their experimenting with new 3D printing materials and technologies, looking for greater competitive strength in their industry. Please click on the graphic to expand for easier reading.

  • North American-based enterprises see the scale to support complex product concepts (complex geometries), speed (quick iterations), scale (mass customizations) and cost savings as the top benefits of 3D printing. Sculpteo’s survey found that North American enterprises are more optimistic about the potential for 3D printing becoming mainstream in a production environment. While budget and physical space are the two most significant barriers enterprises face in adopting 3D printing at scale, their optimistic outlook on the technology’s future is driving greater adoption to the shop floor. Please click on the graphic to expand for easier reading.

Mar 29

The Best Cloud Computing Companies And CEOs To Work For In 2019 Based On Glassdoor

  • Sysdig, Fivetran, Nuxeo, Cloudian, Mendix, StreamSets, Zscaler, Zoho, SAP, OutSystems, Kony, and Netskope are the most likely to be recommended by their employees to friends looking for a cloud computing company to work for in 2019.
  • Cloud platform and development companies dominate the highest rated cloud businesses when indexed by the percent of employees who would recommend their company to a friend.
  • Taken together, the 12 CEOs leading the top-rated cloud computing companies are approved by 98% of employees as of March 3, 2019, on Glassdoor. CEOs in this group include Thomas Hogan of Kony, Paulo Rosado of OutSystems, Bill McDermott of SAP, and Sridhar Vembu of Zoho.

These and many other insights are from an analysis completed today comparing Computer Reseller News’ 100 Coolest Cloud Computing Vendors of 2019 by their respective Glassdoor scores. The Computer Reseller News annual list of the 100 coolest cloud computing vendors is an impartial, 3rd party benchmark of the fastest-growing and most likely to hire cloud businesses expanding today.  By far the most common request from Forbes readers is which cloud computing companies are the best to work for. The goal of this analysis is to provide readers with insights into which cloud computing companies best fit their skills and at the same time have a strong reputation based on feedback from existing employees.

Indexing the most interesting and fastest growing cloud computing companies by their Glassdoor scores and reputations is a great way to begin defining a long-term career growth strategy. One factor not quantified is how well of a fit an applicant is to company culture. Take every opportunity for in-person interviews, read Glassdoor ratings often and observe as much as possible about daily life in companies of interest to see if they are a good fit for your skills and strengths.

Using the 2019 CRN list as a baseline to compare the Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO, the table below is provided. You can find the original dataset here. There are 15 companies on the CRN list that don’t have that many or any entries on Glassdoor, and they are excluded from the rankings shown below. You can find their mention in the original dataset. If the image below is not visible in your browser, you can view the rankings here.

The highest rated CEOs on Glassdoor as of March 3, 2019, include the following. Please click on the graphic and dataset to expand for easier reading.

The original dataset is shown below:

Dec 5

Where Cloud Computing Jobs Will Be In 2019

  • $146,350 is the median salary for cloud computing professionals in 2018.
  • There are 50,248 cloud computing positions available in the U.S. today available from 3,701 employers and 101,913 open positions worldwide today.
  • Oracle (NYSE: ORCL), Deloitte and Amazon (NASDAQ: AMZN) have the most open cloud computing jobs today.
  • Java, Linux, Amazon Web Services (AWS), Software Development, DevOps, Docker and Infrastructure as a Service (IaaS) are the most in-demand skills.
  • Washington DC, Arlington-Alexandria, VA, San Francisco-Oakland-Hayward, CA, New York-Newark-Jersey City, NY, San Jose-Sunnyvale-Santa Clara, CA, Chicago-Naperville-Elgin, IL, are the top five cities where cloud computing jobs are today and will be in 2019.

Demand for cloud computing expertise continues to increase exponentially and will accelerate in 2019. To better understand the current and future direction of cloud computing hiring trends, I utilized Gartner TalentNeuron. Gartner TalentNeuron is an online talent market intelligence portal with real-time labor market insights, including custom role analytics and executive-ready dashboards and presentations. Gartner TalentNeuron also supports a range of strategic initiatives covering talent, location, and competitive intelligence.

Gartner TalentNeuron maintains a database of more than one billion unique job listings and is collecting hiring trend data from more than 150 countries across six continents, resulting in 143GB of raw data being acquired daily. In response to many Forbes readers’ requests for recommendations on where to find a job in cloud computing, I contacted Gartner to gain access to TalentNeuron.

Key takeaways include the following:

  • $146,350 is the median salary for cloud computing professionals in 2018.  Cloud computing salaries have soared in the last two years, with 2016’s median salary being $124,300 a jump of $22,050. The following graphic shows the distribution of salaries for 50,248 cloud computing jobs currently available in the U.S. alone. Please click on the graphic to expand for easier reading.

  • The Hiring Scale is 78 for jobs that require cloud computing skill sets, with the average job post staying open 46 days. The higher the Hiring Scale score, the more difficult it is for employers to find the right applicants for open positions. Nationally an average job posting for an IT professional with cloud computing expertise is open 46 days. Please click on the graphic to expand for easier reading.

  • Washington, DC – Arlington-Alexandria, VA leads the top twenty metro areas that have the most open positions for cloud computing professionals today. Mapping the distribution of job volume, salary range, candidate supply, posting period and hiring scale by Metropolitan Statistical Area (MSA) or states and counties are supported by Gartner TalentNeuron.  The following graphic is showing the distribution of talent or candidate supply.  These are the markets with the highest supply of talent with cloud computing skills.

  • Oracle (NYSE: ORCL), Deloitte and Amazon (NASDAQ: AMZN) have the most open cloud computing jobs today. IBM, VMWare, Capital One, Microsoft, KPMG, Salesforce, PricewaterhouseCoopers, U.S. Bank, and Booz Allen Hamilton, Raytheon Corporation, SAP, Capgemini, Google, Leidos and Nutanix all have over 100 open cloud computing positions today.

Aug 26

Glassdoor’s 10 Highest Paying Tech Jobs Of 2018

  • Software Engineering Manager is the highest paying position with an average salary of $163,500 with 31,621 open positions on Glassdoor today.
  • Over 368,000 open positions are available across the 10 highest paying jobs on Glassdoor today.
  • $147,000 is the average salary of the top 10 tech jobs on Glassdoor today.
  • 12.7% of all open positions are for software engineers, making this job the most in-demand in tech today.

Glassdoor is best known for its candid, honest reviews of employers written anonymously by employees. It is now common practice and a good idea for anyone considering a position with a new employer to check them out on Glassdoor first. With nearly 40 million reviews on more than 770,000 companies. Glassdoor is now the 2nd most popular job site professionals rely on in the U.S., attracting approximately 59 million job seekers a month. The Chief Human Resources Officer of one of the largest and best-known cloud-based enterprise software companies told me recently she gets 2X more applications from Glassdoor for any given position than any other recruiting site or channel.

Earlier this month Glassdoor Economic Research published the results of research completed on how base pay compares between tech and non-tech jobs.  The research team gathered a sample of tech companies with at least 100 job postings on Glassdoor as of June 26, 2018. Glassdoor defined tech roles as those positions requiring knowledge of code, software or data. The study found the following to be the 10 highest paying tech jobs today:

Walmart eCommerce, Microsoft, Intel, Amazon, and Google have the highest concentration of tech jobs as a percentage of all positions open. Workday, Salesforce, Verizon, and IBM have the highest concentration of non-tech positions available today.

Source: Glassdoor Economic Research Blog, Landing a Non-Tech Job in Tech: Who’s Hiring Today? July 19, 2018

May 26

The Best Big Data Companies And CEOs To Work For In 2018

Forbes readers’ most common requests center on who the best companies are to work for in analytics, big data, data management, data science and machine learning. The latest Computer Reseller News‘ 2018 Big Data 100 list of companies is used to complete the analysis as it is an impartial, independent list aggregated based on CRN’s analysis and perspectives of the market. Using the CRN list as a foundation, the following analysis captures the best companies in their respective areas today.

Using the 2018 Big Data 100 CRN list as a baseline to compare the Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO, the following analysis was completed today. 25 companies on the list have very few (less than 15) or no Glassdoor reviews, so they are excluded from the rankings. Based on analysis of Glassdoor score patterns over the last four years, the lower the number of rankings, the more 100% scores for referrals and CEOs. These companies, however, are included in the full data set available here. If the image below is not visible in your browser, you can view the rankings here.

 

The highest rated CEOs on Glassdoor as of May 11, 2018 include the following:

Dataiku Florian Douetteau 100%
StreamSets Girish Pancha 100%
MemSQL Nikita Shamgunov 100%
1010 Data Greg Munves 99%
Salesforce.com Marc Benioff 98%
Attivio Stephen Baker 98%
SAP Bill McDermott 97%
Qubole Ashish Thusoo 97%
Trifacta Adam Wilson 97%
Zaloni Ben Sharma 97%
Reltio Manish Sood 96%
Microsoft Satya Nadella 96%
Cloudera Thomas J. Reilly 96%
Sumo Logic Ramin Sayar 96%
Google Sundar Pichai 95%
Looker Frank Bien 93%
MongoDB Dev Ittycheria 92%
Snowflake Computing Bob Muglia 92%
Talend Mike Tuchen 92%
Databricks Ali Ghodsi 90%
Informatica Anil Chakravarthy 90%

 

May 13

Five Reasons Why Machine Learning Needs To Make Resumes Obsolete

  • Hiring companies nationwide miss out on 50% or more of qualified candidates and tech firms incorrectly classify up 80% of candidates due to inaccuracies and shortcomings of existing Applicant Tracking Systems (ATS), illustrating how faulty these systems are for enabling hiring.
  • It takes on average 42 days to fill a position, and up to 60 days or longer to fill positions requiring in-demand technical skills and costs an average $5,000 to fill each position.
  • Women applicants have a 19% chance of being eliminated from consideration for a job after a recruiter screen and 30% after an onsite interview, leading to a massive loss of brainpower and insight every company needs to grow.

It’s time the hiring process gets smarter, more infused with contextual intelligence, insight, evaluating candidates on their mastery of needed skills rather than judging candidates on resumes that reflect what they’ve achieved in the past. Enriching the hiring process with greater machine learning-based contextual intelligence finds the candidates who are exceptional and have the intellectual skills to contribute beyond hiring managers’ expectations. Machine learning algorithms can also remove any ethic- and gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills.

The hiring process relied on globally today hasn’t changed in over 500 years. From Leonardo da Vinci’s handwritten resume from 1482, which reflects his ability to build bridges and support warfare versus the genius behind Mona Lisa, Last Supper, Vitruvian Man, and a myriad of scientific discoveries and inventions that modernized the world, the approach job seekers take for pursuing new positions has stubbornly defied innovation. ATS apps and platforms classify inbound resumes and provide rankings of candidates based on just a small glimpse of their skills seen on a resume. When what’s needed is an insight into which managerial, leadership and technical skills & strengths any given candidate is attaining mastery of and at what pace.  Machine learning broadens the scope of what hiring companies can see in candidates by moving beyond the barriers of their resumes. Better hiring decisions are being made, and the Return on Investment (ROI) drastically improves by strengthening hiring decisions with greater intelligence. Key metrics including time-to-hire, cost-to-hire, retention rates, and performance all will improve when greater contextual intelligence is relied on.

Look Beyond Resumes To Win The War For Talent

Last week I had the opportunity to speak with the Vice President of Human Resources for one of the leading technology think tanks globally. He’s focusing on hundreds of technical professionals his organization needs in six months, 12 months and over a year from now to staff exciting new research projects that will deliver valuable Intellectual Property (IP) including patents and new products.

Their approach begins by seeking to understand the profiles and core strengths of current high performers, then seek out matches with ideal candidates in their community of applicants and the broader technology community. Machine learning algorithms are perfectly suited for completing the needed comparative analysis of high performer’s capabilities and those of candidates, whose entire digital persona is taken into account when comparisons are being completed. The following graphic illustrates the eightfold.ai Talent Intelligence Platform (TIP), illustrating how integrated it is with publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools. Please click on the graphic to expand it for easier reading.

The comparative analysis of high achievers’ characteristics with applicants takes seconds to complete, providing a list of prospects complete with profiles. Machine learning-derived profiles of potential hires meeting the high performers’ characteristics provided greater contextual intelligence than any resume ever could. Taking an integrated approach to creating the Talent Intelligence Platform (TIP) yields insights not available with typical hiring or ATS solutions today. The profile below reflects the contextual intelligence and depth of insight possible when machine learning is applied to an integrated dataset of candidates. Please click on the graphic to expand it for easier reading. Key elements in the profile below include the following:

  • Career Growth Bell Curve – Illustrates how a given candidate’s career progressions and performance compares relative to others.

  • Social Following On Public Sites –  Provides a real-time glimpse into the candidate’s activity on Github, Open Stack, and other sites where technical professionals can share their expertise. This also provides insight into how others perceive their contributions.

  • Highlights Of Background That Is Relevant To Job(s) Under Review Provides the most relevant data from the candidate’s history in the profile so recruiters and managers can more easily understand their strengths.

  • Recent Publications – Publications provide insights into current and previous interests, areas of focus, mindset and learning progression over the last 10 to 15 years or longer.

  • Professional overlap that makes it easier to validate achievements chronicled in the resume – Multiple sources of real-time career data validate and provide greater context and insight into resume-listed accomplishments.

The key is understanding the context in which a candidate’s capabilities are being evaluated. And a 2-page resume will never give enough latitude to the candidate to cover all bases. For medium to large companies – doing this accurately and quickly is a daunting task if done manually – across all roles, all the geographies, all the candidates sourced, all the candidates applying online, university recruiting, re-skilling inside the company, internal mobility for existing employees, and across all recruitment channels. This is where machine learning can be an ally to the recruiter, hiring manager, and the candidate.

Five Reasons Why Machine Learning Needs To Make Resumes Obsolete

Reducing the costs and time-to-hire, increasing the quality of hires and staffing new initiatives with the highest quality talent possible all fuels solid revenue growth. Relying on resumes alone is like being on a bad Skype call where you only hear every tenth word in the conversation. Using machine learning-based approaches brings greater acuity, clarity, and visibility into hiring decisions.

The following are the five reasons why machine learning needs to make resumes obsolete:

  1. Resumes are like rearview mirrors that primarily reflect the past. What needed is more of a focus on where someone is going, why (what motivates them) and what are they fascinated with and learning about on their own. Resumes are rearview mirrors and what’s needed is an intelligent heads-up display of what their future will look like based on present interests and talent.
  2. By relying on a 500+-year-old process, there’s no way of knowing what skills, technologies and training a candidate is gaining momentum in. The depth and extent of mastery in specific areas aren’t reflected in the structure of resumes. By integrating multiple sources of data into a unified view of a candidate, it’s possible to see what areas they are growing the quickest in from a professional development standpoint.
  3. It’s impossible to game a machine learning algorithm that takes into account all digital data available on a candidate, while resumes have a credibility issue. Anyone who has hired subordinates, staff, and been involved in hiring decisions has faced the disappointment of finding out a promising candidate lied on a resume. It’s a huge let-down. Resumes get often gamed with one recruiter saying at least 60% of resumes have exaggerations and in some cases lies on them. Taking all data into account using a platform like TIP shows the true candidate and their actual skills.
  4. It’s time to take a more data-driven approach to diversity that removes unconscious biases. Resumes today immediately carry inherent biases in them. Recruiter, hiring managers and final interview groups of senior managers draw their unconscious biases based on a person’s name, gender, age, appearance, schools they attended and more. It’s more effective to know their skills, strengths, core areas of intelligence, all of which are better predictors of job performance.
  5. Reduces the risk of making a bad hire that will churn out of the organization fast. Ultimately everyone hires based in part on their best judgment and in part on their often unconscious biases. It’s human nature. With more data the probability of making a bad hire is reduced, reducing the risk of churning through a new hire and costing thousands of dollars to hire then replace them. Having greater contextual intelligence reduces the downside risks of hiring, removes biases by showing with solid data just how much a person is qualified or not for a role, and verifies their background strengths, skills, and achievements. Factors contributing to unconscious biases including gender, race, age or any other factors can be removed from profiles, so candidates are evaluated only on their potential to excel in the roles they are being considered for.

Bottom line: It’s time to revolutionize resumes and hiring processes, moving them into the 21st century by redefining them with greater contextual intelligence and insight enabled by machine learning.

 

Apr 29

How To Close The Talent Gap With Machine Learning

  • 80% of the positions open in the U.S. alone were due to attrition. On an average, it costs $5,000 to fill an open position and takes on average of 2 months to find a new employee. Reducing attrition removes a major impediment to any company’s productivity.
  • The average employee’s tenure at a cloud-based enterprise software company is 19 months; in the Silicon Valley this trends to 14 months due to intense competition for talent according to C-level executives.
  • Eightfold.ai can quantify hiring bias and has found it occurs 35% of the time within in-person interviews and 10% during online or virtual interview sessions.
  • Adroll Group launched nurture campaigns leveraging the insights gained using Eightfold.ai for a data scientist open position and attained a 48% open rate, nearly double what they observed from other channels.
  • A leading cloud services provider has seen response rates to recruiting campaigns soar from 20% to 50% using AI-based candidate targeting in the company’s community.

The essence of every company’s revenue growth plan is based on how well they attract, nurture, hire, grow and challenge the best employees they can find. Often relying on manual techniques and systems decades old, companies are struggling to find the right employees to help them grow. Anyone who has hired and managed people can appreciate the upside potential of talent management today.

How AI and Machine Learning Are Revolutionizing Talent Management

Strip away the hype swirling around AI in talent management and what’s left is the urgent, unmet needs companies have for greater contextual intelligence and knowledge about every phase of talent management. Many CEOs are also making greater diversity and inclusion their highest priority. Using advanced AI and machine learning techniques, a company founded by former Google and Facebook AI Scientists is showing potential in meeting these challenges. Founders Ashutosh Garg and Varun Kacholia have over 6000+ research citations and 80+ search and personalization patents. Together they founded Eightfold.ai as Varun says “to help companies find and match the right person to the right role at the right time and, for the first time, personalize the recommendations at scale.” Varun added that “historically, companies have not been able to recognize people’s core capabilities and have unnecessarily exacerbated the talent crisis,” said Varun Kacholia, CTO, and Co-Founder of Eightfold.ai.

What makes Eightfold.ai noteworthy is that it’s the first AI-based Talent Intelligence Platform that combines analysis of publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools and spreadsheets then creates ontologies based on organization-specific success criteria. Each ontology, or area of talent management interest, is customizable for further queries using the app’s easily understood and navigated user interface.

Based on conversations with customers, its clear integration is one of the company’s core strengths. Eightfold.ai relies on an API-based integration strategy to connect with legacy back-end systems. The company averages between 2 to 3 system integrations per customer and supports 20 unique system integrations today with more planned. The following diagram explains how the Eightfold Talent Intelligence Platform is constructed and how it works.

For all the sophisticated analysis, algorithms, system integration connections, and mathematics powering the Eightfold.ai platform, the company’s founders have done an amazing job creating a simple, easily understood user interface. The elegant simplicity of the Eightfold.ai interface reflects the same precision of the AI and machine learning code powering this platform.

I had a chance to speak with Adroll Group and DigitalOcean regarding their experiences using Eightfold.ai. Both said being able to connect the dots between their candidate communities, diversity and inclusion goals, and end-to-end talent management objectives were important goals that the streamlined user experience was helping enable. The following is a drill-down of a candidate profile, showing the depth of external and internal data integration that provides contextual intelligence throughout the Eightfold.ai platform.

Talent Management’s Inflection Point Has Arrived 

Every interaction with a candidate, current associate, and high-potential employee is a learning event for the system.

AI and machine learning make it possible to shift focus away from being transactional and more on building relationships. AdRoll Group and DigitalOcean both mentioned how Eightfold.ai’s advanced analytics and machine learning helps them create and fine-tune nurturing campaigns to keep candidates in high-demand fields aware of opportunities in their companies. AdRoll Group used this technique of concentrating on insights to build relationships with potential Data Scientists and ultimately made a hire assisted by the Eightold.ai platform. DigitalOcean is also active using nurturing campaigns to recruit for their most in-demand positions. “As DigitalOcean continues to experience rapid growth, it’s critical we move fast to secure top talent, while taking time to nurture the phenomenal candidates already in our community,” said Olivia Melman, Manager, Recruiting Operations at DigitalOcean. “Eightfold.ai’s platform helps us improve operational efficiencies so we can quickly engage with high quality candidates and match past applicants to new openings.

In companies of all sizes, talent management reaches its full potential when accountability and collaboration are aligned to a common set of goals. Business strategies and new business models are created and the specific amount of hires by month and quarter are set. Accountability for results is shared between business and talent management organizations, as is the case at AdRoll Group and DigitalOcean, both of which are making solid contributions to the growth of their businesses. When accountability and collaboration are not aligned, there are unpredictable, less than optimal results.

AI makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. The company’s founders call this aspect of their platform personalization at scale. “Our platform takes a holistic approach to talent management by meaningfully connecting the dots between the individual and the business. At Eightfold.ai, we are going far beyond keyword and Boolean searches to help companies and employees alike make more fulfilling decisions about ‘what’s next, “ commented Ashutosh Garg, CEO, and Co-Founder of Eightfold.ai.

Every hiring manager knows what excellence looks like in the positions they’re hiring for. Recruiters gather hundreds of resumes and use their best judgment to find close matches to hiring manager needs. Using AI and machine learning, talent management teams save hundreds of hours screening resumes manually and calibrate job requirements to the available candidates in a company’s candidate community. This graphic below shows how the Talent Intelligence Platform (TIP) helps companies calibrate job descriptions. During my test drive, I found that it’s as straightforward as pointing to the profile of ideal candidate and asking TIP to find similar candidates.

Achieving Greater Equality With A Data-Driven Approach To Diversity

Eightfold.ai can quantify hiring bias and has found it occurs 35% of the time within in-person interviews and 10% during online or virtual interview sessions. They’ve also analyzed hiring data and found that women are 11% less like to make it through application reviews, 19% less likely through recruiter screens, 12% through assessments and a shocking 30% from onsite interviews. Conscious and unconscious biases of recruiters and hiring managers often play a more dominant role than a woman’s qualifications in many hiring situations. For the organizations who are enthusiastically endorsing diversity programs yet struggling to make progress, AI and machine learning are helping to accelerate them to the goals they want to accomplish.

AI and machine learning can’t make an impact in this area quickly enough. Imagine the lost brainpower from not having a way to evaluate candidates based on their innate skills and potential to excel in the role and the need for far greater inclusion across the communities companies operate in. AdRoll Group’s CEO is addressing this directly and has made attaining greater diversity and inclusion a top company objective for the year. Daniel Doody, Global Head of Talent at AdRoll Group says “We’re very deliberate in our efforts to uncover and nurture more diverse talent while also identifying individuals who have engaged with our talent brand to include them” he said. Daniel Doody continued, “Eightfold.ai has helped us gain greater precision in our nurturing campaigns designed to bring more diverse talent to Adroll Group globally.”

Kelly O. Kay, Managing Partner, Global Managing Partner, Software & Internet Practice at Heidrick & Struggles agrees. “Eightfold.ai levels the playing field for diversity hiring by using pattern matching based on human behavior, which is fascinating,” Mr. Kay said. He added, “I’m 100% supportive of using AI and machine learning to provide everyone equal footing in pursuing and attaining their career goals.” He added that the Eightfold.ai’s greatest strength is how brilliantly it takes on the challenge of removing unconscious bias from hiring decisions, further ensuring greater diversity in hiring, retention and growth decisions.

Eightfold.ai has a unique approach to presenting potential candidates to recruiters and hiring managers. They can remove any gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills. And the platform also can create gender-neutral job descriptions in seconds too. With these advances in AI and machine learning, long-held biases of tech companies who only want to hire from Cal-Berkeley, Stanford or MIT are being challenged when they see the quality of candidates from just as prestigious Indian, Asian, and European universities as well. Daniel Doody of Adroll Group says the insights gained from the Eightfold.ai platform “are helping to make managers and recruiters more aware of their own hiring biases while at the same time assisting in nurturing potential candidates via less obvious channels.”

How To Close The Talent Gap

Based on conversations with customers, it’s apparent that Eightfold.ai’s Talent Intelligence Platform (TIP) provides enterprises the ability to accelerate time to hire, reduce the cost to hire and increase the quality of hire. Eightfold.ai customers are also seeing how TIP enables their companies to reduce employee attrition, saving on hiring and training costs and minimizing the impact of lost productivity. Today more CEOs and CFOs than ever are making diversity and talent initiatives their highest priority. Based on conversations with Eightfold.ai customers it’s clear their TIP provides the needed insights for C-level executives to reach their goals.

Another aspect of the TIP that customers are just beginning to explore is how to identify employees who are the most likely to leave, and take proactive steps to align their jobs with their aspirations, extending the most valuable employees’ tenure at their companies. At the same time, customers already see good results from using TIP to identify top talent that fits open positions who are likely to join them and put campaigns in place to recruit and hire them before they begin an active job search. Every Eightfold.ai customer spoken with attested to the platform’s ability to help them in their strategic imperatives around talent.

Jan 7

Data Science And Machine Learning Jobs Most In-Demand on LinkedIn

  • Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn.
  • Data scientist roles have grown over 650% since 2012, but currently, 35,000 people in the US have data science skills, while hundreds of companies are hiring for those roles.
  • There are currently 1,829 open Machine Learning Engineering positions on LinkedIn.
  • Job growth in the next decade is expected to outstrip growth during the previous decade, creating 11.5M jobs by 2026, according to the U.S. Bureau of Labor Statistics.

These and many other insights are from the recently released LinkedIn 2017 U.S. Emerging Jobs Report. LinkedIn has provided an overview of the methodology in their post, The Fastest-Growing Jobs in the U.S. Based on LinkedIn Data. “Emerging jobs” refers to the job titles that saw the largest growth in frequency over that five year period. LinkedIn reports that based on their analysis, the job market in the U.S. is brimming right now with fresh and exciting opportunities for professionals in a range of emerging roles.

Key takeaways from the study include the following:

  • There are 9.8 times more Machine Learning Engineers working today than five years ago based on LinkedIn’s research, with 1,829 open positions listed on the site today. There are 6.5 times more Data Scientists than five years ago, and 5.5 times more Big Data Developers. The following graphic illustrates the rapid growth of key data scient, machine leanring, big data and full stack developers in addition to sales development and customer success managers.

  • Software engineering is a common starting point for professionals who are in the top five fasting growing jobs today. The career path to Machine Learning Engineer and Big Data Developer begins with a solid software engineering background. The top five highest growth job typical career paths are shown below:

  • The skills most strongly represented across the 20 fastest growing jobs include management, sales, communication, and marketing. Additional skills represented across the highest growing jobs include marketing expertise (analytics and marketing automation), start-ups, Python, software development, analytics, cloud computing and knowledge of retail systems.
  • LinkedIn interviewed 1,200 hiring managers to determine which soft skills are most in-demand and adaptability came out on top. Additional soft skills include culture fit, collaboration, leadership, growth potential, and prioritization.

Sources:

LinkedIn Blog: The Fastest-Growing Jobs in the U.S. Based on LinkedIn Data

LinkedIn’s 2017 U.S. Emerging Jobs Report

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